Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with similar to 100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

Automated summary

Liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics is a powerful tool for studying both known and unknown metabolites. However, annotating unknown metabolites remains a challenge. In this study, a knowledge-guided multi-layer network (KGMN) approach is developed to achieve global metabolite annotation from knowns to unknowns. The KGMN approach integrates three-layer networks and has been successfully applied to annotate putative unknowns in in vitro enzymatic reaction systems and different biological samples. This method enables efficient unknown annotations and advances the discovery of recurrent unknown metabolites.